{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 导入包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gymnasium as gym\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "import numpy as np\n",
    "import rl_utils\n",
    "import os\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 策略网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PolicyNet(torch.nn.Module):\n",
    "    def __init__(self, state_dim, hidden_dim, action_dim):\n",
    "        super(PolicyNet, self).__init__()\n",
    "        self.fc1 = torch.nn.Linear(state_dim, hidden_dim)\n",
    "        self.fc2 = torch.nn.Linear(hidden_dim, action_dim)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = self.fc2(x)\n",
    "        return F.softmax(x, dim=1)  # 输出各动作的概率"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# REIFORCE算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class REINFORCE:\n",
    "    def __init__(self, state_dim, hidden_dim, action_dim, learning_rate, gamma,\n",
    "                 device):\n",
    "        self.policy_net = PolicyNet(state_dim, hidden_dim,\n",
    "                                    action_dim).to(device)\n",
    "        self.optimizer = torch.optim.Adam(self.policy_net.parameters(), \n",
    "                                          lr=learning_rate)  # 使用Adam优化器\n",
    "        self.gamma = gamma  # 折扣因子\n",
    "        self.device = device\n",
    "    def take_action(self, state):  # 根据动作概率分布随机采样\n",
    "        state = torch.tensor(state[np.newaxis, :], dtype=torch.float).to(self.device)\n",
    "        probs = self.policy_net(state)  # 给state加一个维度\n",
    "        action_dist = torch.distributions.Categorical(probs)  # 根据probs给出的概率创建一个类别分布\n",
    "        action = action_dist.sample()  # 从创建的类别分布中抽样, 得到某个类别的序号\n",
    "        return action.item()\n",
    "\n",
    "    def update(self, transition_dict):\n",
    "        return_list = transition_dict['rewards']\n",
    "        state_list = transition_dict['states']\n",
    "        action_list = transition_dict['actions']\n",
    "\n",
    "        G = 0\n",
    "        self.optimizer.zero_grad() # 设定0梯度\n",
    "        for i in reversed(range(len(return_list))):  # 从最后一步算起, 对每一个状态动作计算损失\n",
    "            reward = return_list[i]\n",
    "            state = torch.tensor(state_list[i], dtype=torch.float).to(self.device)\n",
    "            action = torch.tensor(action_list[i]).view(-1, 1).to(self.device)  # 选择当前步骤的动作\n",
    "            # 依据动作选概率, 对数化\n",
    "            log_prob = torch.log(self.policy_net(state.unsqueeze(0)).gather(1, action))\n",
    "            G = self.gamma * G + reward\n",
    "            loss = -log_prob * G  # 每一步的损失函数\n",
    "            loss.backward()  # 反向传播计算梯度, 在循环中一直积累梯度\n",
    "        self.optimizer.step()  # 梯度下降, 每轮游戏结束后计算的总损失梯度全部更新一次"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ✅注意点"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "策略梯度的损失，不是与正确的动作来对比得到交叉熵，因为我们事先不知道正确的动作，所以没有参考，因此就只能人为确定一种损失，例如用网络给出的各个动作的概率，乘以当前得到的奖励，如果奖励比较低，乘起来也比较小，我们希望提升奖励，所以加个负号再执行梯度下降，等于是做了梯度上升，也就希望提升奖励，因为概率肯定是正的，所以是提升奖励，那么奖励越小，反之同理，符合我们调整参数的目标。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 参数初始化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模型相关\n",
    "s_epoch = 0\n",
    "total_epoch = 5\n",
    "s_episode = 0\n",
    "total_episode = 1000\n",
    "torch.manual_seed(0)\n",
    "best_score = 0\n",
    "return_list = []\n",
    "gamma = 0.98  # 折扣率\n",
    "learning_rate = 1e-3  # 学习率\n",
    "env_name = \"CartPole-v1\"\n",
    "env = gym.make(env_name)\n",
    "\n",
    "# 神经网络相关\n",
    "hidden_dim = 128\n",
    "state_dim = env.observation_space.shape[0]\n",
    "action_dim = env.action_space.n\n",
    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "\n",
    "agent = REINFORCE(state_dim, hidden_dim, action_dim, \n",
    "                  learning_rate, gamma, device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 读取检查点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_ckp(ckp_path):\n",
    "    if os.path.exists(ckp_path):\n",
    "        checkpoint = torch.load(ckp_path)\n",
    "        s_epoch = checkpoint['epoch']\n",
    "        s_episode = checkpoint['episode']\n",
    "        agent.policy_net.load_state_dict(checkpoint['best_weight'])\n",
    "        return_list = checkpoint['return_list']\n",
    "        return s_epoch, s_episode, return_list\n",
    "    else:\n",
    "        s_epoch = 0\n",
    "        s_episode = 0\n",
    "        return_list = []\n",
    "        return s_epoch, s_episode, return_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "CKP_PATH = 'checkpoints/REINFORCE_CPv1.pt'\n",
    "s_epoch, s_episode, return_list = read_ckp(CKP_PATH)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 训练策略"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                               \r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for epoch in range(s_epoch, total_epoch):\n",
    "    with tqdm(total=(total_episode - s_episode), desc='<%d/%d>' % (epoch + 1, total_epoch), \n",
    "              leave=False) as pbar:\n",
    "        for episode in range(s_episode, total_episode):\n",
    "            episode_reward = 0\n",
    "            transition_dict = {\n",
    "                'states': [],\n",
    "                'actions': [],\n",
    "                'next_states': [],\n",
    "                'rewards': [],\n",
    "                'dones': [],\n",
    "                'truncated' : [],\n",
    "            }\n",
    "            state = env.reset()[0]\n",
    "            done = truncated = False\n",
    "            while not (done | truncated):\n",
    "                action = agent.take_action(state)\n",
    "                next_state, reward, done, truncated, _ = env.step(action)\n",
    "                transition_dict['states'].append(state)\n",
    "                transition_dict['actions'].append(action)\n",
    "                transition_dict['next_states'].append(next_state)\n",
    "                transition_dict['rewards'].append(reward)\n",
    "                transition_dict['dones'].append(done)\n",
    "                transition_dict['truncated'].append(truncated)\n",
    "                \n",
    "                state = next_state\n",
    "                episode_reward += reward\n",
    "            return_list.append(episode_reward)\n",
    "            agent.update(transition_dict)\n",
    "            \n",
    "            if episode_reward > best_score:\n",
    "                best_weight = agent.policy_net.state_dict()\n",
    "                best_score = episode_reward\n",
    "            \n",
    "            if (episode + 1) % 10 == 0:\n",
    "                pbar.set_postfix({\n",
    "                    'episode':\n",
    "                    '%d' % (total_episode * epoch + episode + 1),\n",
    "                    'recent_return':\n",
    "                    '%.3f' % np.mean(return_list[-10:])\n",
    "                })\n",
    "            \n",
    "            torch.save({\n",
    "            'epoch': epoch,\n",
    "            'episode': episode,\n",
    "            'best_weight': best_weight,\n",
    "            'return_list': return_list,\n",
    "            }, CKP_PATH)\n",
    "                \n",
    "            pbar.update(1)\n",
    "        s_episode = 0\n",
    "        \n",
    "agent.policy_net.load_state_dict(best_weight)  # 应用最佳权重"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rl_utils.picture_return(return_list, 'REINFORCE', env_name, 9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
